DataDock is an advanced data integration and management platform
99.5%

reliability success rate, 92% fewer errors

90%

faster historical data migration

87%

business impact improved insights, real-time data

99.5%

reliability success rate, 92% fewer errors

90%

faster historical data migration

87%

business impact improved insights, real-time data

ABOUT PROJECT
This platform designed to streamline automated data synchronization across multiple business systems. The platform serves as a centralized hub for connecting, monitoring, and managing data flows between various enterprise applications and databases.
Location:
Ukraine
Project:
DataDock
Date:
2025-present
Industry:
SaaS, Data Integration Platform
CLIENT & Business context

Organizations often struggle with manual data synchronization across multiple systems, leading to inconsistent information, time-consuming processes, and operational inefficiencies.

We developed DataDock to solve this problem by providing automated data integration with real-time monitoring, intelligent ETL processes, and seamless connectivity between diverse business applications.

As a result, companies eliminate manual data entry, reduce synchronization errors, and achieve consistent, up-to-date information across their entire technology ecosystem.

Goal & Constraints
From growing complexity to an advanced management platform
01/
Automate data synchronization across diverse business systems and databases
02/
Provide real-time monitoring and progress tracking for all data integration processes
03/
Build a scalable platform that can handle large volumes of data with minimal downtime
04/
Ensure data consistency and integrity across all connected systems
Product approach
The domain analysis covered the following aspects:
01/
Enterprise data integration patterns and ETL/ELT workflows in Danish business environment
02/
Real-time vs. batch processing requirements for different data types and systems
03/
Data consistency and monitoring challenges across distributed enterprise applications
04/
Scalability requirements for handling large historical datasets with incremental updates
Technical Architecture

Core Stack & Infrastructure

  • Backend: Serverless with AWS Lambda, Step Functions
  • Frontend: Next.js 15, React 19, TypeScript
  • Database: PostgreSQL, Supabase for real-time operations
  • Cloud Infrastructure: AWS-native components for auto-scaling and cost optimization
  • Integration: Custom ETL orchestration with multi-system API connectors

System Modules

UI/UX Features

Integrations

Key Features Built:

  • Automated Sync: Intelligent incremental/full transfers with real-time tracking
  • Multi-Source Integration: POS, Shopify, CRM, custom DBs with standardized transformation
  • Real-Time Dashboard: Live progress, detailed status, error handling
  • Scalable Architecture: Serverless AWS ETL with batching & parallel processing
JTBD
UI/UX Features
Planned features
01/

Advanced Integration Management. Multi-tenant admin panel with comprehensive connection analytics, sync performance monitoring, and organization-wide data pipeline oversight capabilities.

02/

MCP Server Integration: Model Context Protocol server implementation for enhanced AI-driven data mapping and intelligent schema recognition across different data sources.

03/

Enterprise Security Center: Advanced audit logging, role-based access controls, and comprehensive compliance reporting for GDPR and other regulatory requirements.

04/
05/
Tech Stack & Tools Used
AWS Lambda
Next
React
TypeScript
PostgreSQL
AWS

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  • vlad
    Vladyslav Tamashchuk
    CEO | Founder
    $100
    Consultation fee
  • max
    Max Blokhin
    Account Manager, Chief Sales Officer
    $0
    Intro call
  • oleh
    Oleh Chervinskyi
    Upwork Manager – Leadgen & Sales
    $0
    Intro call